Clouds play a tangible role in the Earth's atmosphere and in particular, the cloud base height (CBH), which is linked to cloud type, is one of the most important characteristics to describe the influence of clouds on the environment. In the present study, CBH observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad (23.03° N, 72.54° E), India. A detailed comparison has been performed with the use of ground-based CBH measurements from Ceilometer CL31 and CBH retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard Aqua and Terra satellite. CBH retrieved from MODIS is ∼ 1.955 and ∼ 1.093 km on 25 July 2014 and 1 January 2015 respectively, which matches well with ceilometer-measured CBH ( ∼ 1.92 and ∼ 1.097 km). Some interesting features of cloud dynamics viz. strong downdraft and updraft have been observed over Ahmedabad which revealed different cloud characteristics during monsoon and post-monsoon periods. CBH shows seasonal variation during the Indian summer monsoon and post-monsoon period. Results indicate that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds, and the MODIS satellite provides accurate retrieval of high-level clouds over this region. The CBH algorithm used for the MODIS satellite is also able to capture the low-level clouds.

The high-spectral-resolution AIRS (Atmospheric InfraRed Sounder) instrument onboard the NASA (National Aeronautics and Space Administration) Earth Observing System (EOS)-Aqua satellite represents the most advanced sounding system in space and provides unprecedented wealth of highly accurate radiance measurements. This paper describes a standalone and fast single field-of-view (FOV) algorithm to retrieve atmospheric sounding profiles (temperature, humidity, ozone) and surface parameters (surface skin temperature, surface emissivity) from AIRS Level 1B (L1B) clear only infrared radiance measurements. The retrieval algorithm is part of the International MODIS (Moderate Resolution Imaging Spectroradiometer)/AIRS Processing Package (IMAPP) software package, which provides international users with the capability of receiving and processing direct broadcast data in real-time. The IMAPP AIRS retrieval algorithm is based on principal component regression to obtain fast and accurate estimates of the atmospheric state at single FOV. This algorithm is designed specifically for real-time direct broadcast applications where sounding products can be processed efficiently at highest possible spatial resolution. Simulated radiance data is trained on a global set of profiles, representative of a wide variety of atmospheric scenes, which makes the algorithm globally applicable. The results presented and discussed in this paper demonstrate that the IMAPP AIRS retrieval product is rigorously evaluated by various product sources such as numerical weather prediction model analysis fields, retrieved parameters from the operational AIRS L2 product and data from other instruments.

Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales. Satellite-based microwave radiometric observations are considered to be the best because of their high sensitivity to soil moisture, apart from possessing all-weather and day–night observation capabilities with high repetitousness. In the present study, 6.6-GHz horizontal-polarization brightness temperature data from the Multifrequency Scanning Microwave Radiometer (MSMR) onboard the Indian Remote Sensing Satellite IRS-P4 have been used for the estimation of large-area-averaged soil wetness. A methodology has been developed for the estimation of soil wetness for the period of June–July from the time series of MSMR brightness temperatures over India. Maximum and minimum brightness temperatures for each pixel are assigned to the driest and wettest periods, respectively. A daily soil wetness index over each pixel is computed by normalizing brightness temperature observations from these extreme values. This algorithm has the advantage that it takes into account the effect of time-invariant factors, such as vegetation, surface roughness, and soil characteristics, on soil wetness estimation. Weekly soil wetness maps compare well to corresponding weekly rainfall maps depicting clearly the regions of dry and wet soil conditions. Comparisons of MSMR-derived soil wetness with in situ observations show a high correlation (R > 0.75), with a standard error of the soil moisture estimate of less than 7% (volumetric unit) for the surface (0–5 cm) and subsurface (5–10 cm) soil moisture.